jfoster17 / extinction-distances Goto Github PK

Add some tests

Currently, the contouring code will produce a set of contours in a fairly large region around the target position. Often, there is one contour associated with the cloud, and a bunch of other contours around other clouds at the edge of the field. In the maser study we selected the correct cloud contour (and edited it, if necessary) by hand. Although we may need to look at the contours by hand, as a first pass we should only select the central contour. Perhaps we can only select the contour that encompasses the central position of the target region/cloud?

Recognize when there are foreground blue clusters producing an incorrect distance estimate. This could be semi-automated by looking at the density of blue stars inside the contour and removing any regions that are significant (high) outliers. Tricky to get right...

Display un-used contours in white and selected contour in cyan to better see structure of the continuum.

Do we want to have the J-K color cut come from a dynamic examination of the histogram rather than at a fixed value?

Most parts of the analysis can be put into BaseDistObj.

Currently, the code uses some extraneous packages (such as astLib) for minor tasks that can probably be done in astropy or should just be done by hand in order to reduce the number of external packages required to a reasonable number.

We can do everything in astropy now and remove one unnecessary package.

Current error estimate is crude Gaussian approximation. Change to Poisson and figure out how to account for completeness correction?

Consider applying variable extinction as a function of Galactic longitude to account for, e.g., spiral arms.

Currently the completeness code applies corrections over a fixed K range (K < 17). But we should not consider stars with K > 16 if these stars have a very low completeness, otherwise we get vast fluctuations in the distance from a small number of stars.

Use astroquery to get UKIDSS image data, catalogs, and Besancon models.